The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Artificial intelligence: a new synthesis
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Agile software development
Bayesian Networks and Decision Graphs
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Generic Object Recognition: Building and Matching Coarse Descriptions from Line Drawings
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
AFPAC '00 Proceedings of the Second International Workshop on Algebraic Frames for the Perception-Action Cycle
Perceptual Components for Context Aware Computing
UbiComp '02 Proceedings of the 4th international conference on Ubiquitous Computing
Evaluating Integrated Speech- and Image Understanding
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
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Proceedings of the 5th international conference on Multimodal interfaces
An XML Based Framework for Cognitive Vision Architectures
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Integration Frameworks for Large Scale Cognitive Vision Systems - An Evaluative Study
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
CVML - An XML-based Computer Vision Markup Language
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Combining Sensory and Symbolic Data for Manipulative Gesture Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Multimodal interaction in an augmented reality scenario
Proceedings of the 6th international conference on Multimodal interfaces
Serial multiple classifier systems exploiting a coarse to fine output coding
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Integrating context-free and context-dependent attentional mechanisms for gestural object reference
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Approaches and Challenges for Cognitive Vision Systems
Creating Brain-Like Intelligence
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Object recognition is the ability of a system to relate visual stimuli to its knowledge of the world. Although humans perform this task effortlessly and without thinking about it, a general algorithmic solution has not yet been found. Recently, a shift from devising isolated recognition techniques towards integrated systems could be observed [Y. Aloimonos, Active vision revisited, in: Y. Aloimonos (Ed.), Active Perception, Lawrence Efibaum, 1993, pp. 1-18; H. Christensen, Cognitive (vision) systems, ERCIM News (April, 2003). 17-18]. The visual active memory (VAM) perspective refines this system view towards an interactive computational framework for recognition systems in human everyday environments. VAM is in line with the recently emerged Cognitive Vision paradigm [H. Christensen, Cognitive (vision) systems, ERCIM News (April, 2003). 17-18] which is concerned with vision systems that evaluate, gather and integrate contextual knowledge for visual analysis. It consists of active processes that generate knowledge by means of a tight cooperation of perception, reasoning, learning and prior models. In addition, VAM emphasizes the dynamic representation of gathered knowledge. The memory is assumed to be structured in a hierarchy of successive memory systems that mediate the modularly defined processing components of the recognition system. Recognition and learning take place in the stress field of objects, actions, activities, scene context, and user interaction. In this paper, we exemplify the VAM perspective by means of existing demonstrator systems. Assuming three different perspectives (biological foundation, system engineering, and computer vision), we will show that the VAM concept is central to the cognitive capabilities of the system and that it leads to a more general object recognition framework.